Genetic algorithms are a search technique based on Darwinian principles of natural selection and genetics. They maintain a population of candidate solutions and evolve them through selection, crossover and mutation to find optimal or near-optimal solutions. Originally developed by John Holland in the 1960s, genetic algorithms have been widely applied to problems that are difficult to solve with traditional techniques. A genetic algorithm initializes a population, evaluates fitness, selects parents for reproduction, performs crossover and mutation on offspring, then iterates the process until a termination condition is reached.

1. INTRODUCTION TO GENETIC
ALGORITHMS
Presented By:
Shadan Alam Shadab (16/IEE/050)
EED, SOE, BLOCK-A
Gautam Buddha University, Gr. Noida, U.P.
Date: 17th November, 2020

2. CONTENTS
• Introduction
• History Of Genetic Algorithms
• What Is Evolution In The Real World?
• Basic Structure Of Soft Computing
• Soft Computing
• Evolutionary Computation
• Genetic Algorithm
• Basic Process
• Advantages
• Disadvantages
• Applications
• References

3. INTRODUCTION
• Genetic Algorithm (GA) is a search-based optimization technique based on the
principles of Genetics and Natural Selection.
• It is frequently used to find optimal or near-optimal solutions to difficult problems
which otherwise would take a lifetime to solve.
• It was inspired by the biological evolution process.
• Uses concepts of “Natural Selection” and “Genetic Inheritance” (Darwin 1859).
• Originally developed by Prof. John Holland (1975).
• Particularly well suited for hard problems where little is known about the underlying
search space.

4. • Widely used in searching techniques involving “search for solutions”.
• A genetic algorithm maintains a population of candidate solutions for the problem at
hand ,and makes it evolve by iteratively applying a set of stochastic operators.

5. HISTORY OF GENETIC ALGORITHMS
• The GA, developed by John Holland and his collaborators in the 1960s and 1970s.
• As early as 1962, John Holland’s work on adaptive systems laid the foundation for
later developments.
• By the 1975, the publication of the book “Adaptation in Natural and Artificial
Systems”, by Holland and his students and colleagues.
• The GA got popular in the late 1980s by was being applied to a broad range of
subjects that are not easy to solve using other techniques.
• In 1992, John Koza has used genetic algorithm to evolve programs to perform
certain tasks. He called his method “genetic programming”

6. WHAT IS EVOLUTION IN THE REAL WORLD?
• For thousands of years, humans have acted as agents of genetic selection, by
breeding offspring with desired traits. All our domesticated animals and food crops
are the results.
• Let review the genetic terms in nature as follows:
• Each cell of a living thing contains chromosomes — strings of DNA.
• Each chromosome contains a set of genes — blocks of DNA.
• Each gene determines some aspect of the organism (like eye colour).
• A collection of genes is sometimes called a genotype.
• A collection of aspects (like eye colour) is sometimes called a phenotype.

7. • Reproduction (crossover) involves recombination of genes from parents and then
small amounts of mutation (errors) in copying.
• The fitness of an organism is how much it can reproduce before it dies.
• Evolution based on “survival of the fittest”.

8. BASIC STRUCTURE OF SOFT COMPUTING
Soft
computing
Fuzzy
System
Neural
Network
Evolutionary
Computation
Machine
learning
Genetic
Algorithm
Genetic
Programming

9. SOFT COMPUTING
• It is another field of computer science.
• It is the fusion of methodologies that were designed to model and enable solutions to real
world problems, which are not modeled, or too difficult to model, mathematically.
• It is associated with fuzzy, complex and dynamic system with uncertain parameters.
• Quality of Service Networking is an example of the class of complex, fuzzy and dynamic
systems with uncertain parameters, which soft computing is intended to model and compute.

10. EVOLUTIONARY COMPUTATION
• Evolutionary computation simulates evolution on a computer.
• The result of such a simulation is a series of optimization algorithms,
usually based on a simple set of rules.
• Optimization iteratively improves the quality of solutions until an optimal, or at
least feasible, solution is found.
• Evolutionary Computations can be studied under 2 categories:
• Genetic Algorithm
• Genetic programming

11. GENETIC ALGORITHM
• A Genetic Algorithm is, a search procedure based on the mechanics of natural
selection and genetics.

12. BASIC PROCESS
• Initialization Population
• Evaluation
• Selection
• Crossover
• Mutation

13. • Initialize population: genetic algorithms begin by initializing a Population of
candidate solutions. This is typically done randomly to provide even coverage of the
entire search space. A candidate solution is a Chromosome that is characterized by a
set of parameters known as Genes.
• Evaluation: Next, the population is evaluated by assigning a fitness value to each
individual in the population. In this stage we would often want to take note of the
current fittest solution, and the average fitness of the population.
After evaluation, the algorithm decides whether it should terminate the search
depending on the termination conditions set. Usually this will be because the
algorithm has reached a fixed number of generations or an adequate solution has
been found.
When the termination condition is finally met, the algorithm will break out of the
loop and typically return its finial search results back to the user.

14. • Selection: if the termination condition is not met, the population goes through a
selection stage in which individuals from the population are selected based on their
fitness score, the higher the fitness, the better chance an individual has of being
selected.
Two pairs of selected individuals called parents.
• Crossover: the next stage is to apply crossover and mutation to the selected
individuals. This stage is where new individuals (children) are created for the next
generation.

15. • Mutation: at this point the new population goes back to the evaluation step and the
process starts again. We call each cycle of this loop a generation.

16. ADVANTAGES
• Solution Space is explored in multiple directions.
• Nonlinear problems -Large Solution space, but GA are ideal.
• Works on complex landscape (discontinuous, noisy, changing with time)
• GA can manipulate many parameters simultaneously .
• GA don't have specific knowledge of problem. All possible search pathways are
considered in GA.

17. DISADVANTAGES
• Computationally expensive and time consuming.
• Issues in representation of problem.
• Proper writing of fitness function.
• Proper values of size of population, crossover and mutation rate.
• Premature Convergence.

18. APPLICATIONS
• To search different combinations of materials.
• To design the perfect combination of stronger, lighter and overall, better final
product.
• To design computer algorithms.
• To schedule tasks.
• To solve other optimization problems easily.

19. REFERENCES
• An Introduction to Genetic Algorithms: https://towardsdatascience.com/an-introduction-to-
genetic-algorithms
• Introduction to Genetic Algorithm: http://www.slideshare.net/circles808/demonstration1-g-
as?qid=adbab8ac-a1c1-4ed8-92b7-1e17e5525316&v=&b=&from_search=8
• Neural Networks, Fuzzy Logic, And Genetic Algorithm:Synthesis And Application
• S.Rajasekaran
• G.A.Vijayalakshmi Pai